你不需要这么做。你可以合并
np.nonzero
,
np.polyfit
和
np.polyval
做这个。看起来是这样的:
import numpy as np
from matplotlib import pyplot as plt
# in your case, you would read your image
# > cv2.imread(...) # import cv2 before
# but we are going to create an image based on a polynomial
img = np.zeros((400, 400), dtype=np.uint8)
h, w = img.shape
xs = np.arange(150, 250)
ys = np.array(list(map(lambda x: 0.01 * x**2 - 4*x + 600, xs))).astype(np.int)
img[h - ys, xs] = 255
# I could use the values I have, but if you have a binary image,
# you will need to get them, and you could do something like this
ys, xs = np.nonzero(img) # use (255-img) if your image is inverted
ys = h - ys
# compute the coefficients
coefs = np.polyfit(xs, ys, 2)
xx = np.arange(0, w).astype(np.int)
yy = h - np.polyval(coefs, xx)
# filter those ys out of the image, because we are going to use as index
xx = xx[(0 <= yy) & (yy < h)]
yy = yy[(0 <= yy) & (yy < h)].astype(np.int) # convert to int to use as index
# create and display a color image just to viz the result
color_img = np.repeat(img[:, :, np.newaxis], 3, axis=2)
color_img[yy, xx, 0] = 255 # 0 because pyplot is RGB
f, ax = plt.subplots(1, 2)
ax[0].imshow(img, cmap='gray')
ax[0].set_title('Binary')
ax[1].imshow(color_img)
ax[1].set_title('Polynomial')
plt.show()
结果如下:
如果你打印
coefs
,你将拥有
[ 1.00486819e-02 -4.01966712e+00 6.01540472e+02]
非常接近
[0.01, -4, 600]
我们选择了。
